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Empirical Inference Probabilistic Numerics Conference Paper Convergence Guarantees for Adaptive Bayesian Quadrature Methods Kanagawa, M., Hennig, P. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Empirical Inference Probabilistic Numerics Conference Paper Limitations of the empirical Fisher approximation for natural gradient descent Kunstner, F., Hennig, P., Balles, L. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX
Probabilistic Numerics Conference Paper Active Multi-Information Source Bayesian Quadrature Gessner, A. G. J. M. M. Proceedings 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), 712-721, (Editors: Adams, RP; Gogate, V), UAI, July 2019 (Published) URL BibTeX
Probabilistic Numerics Empirical Inference Conference Paper DeepOBS: A Deep Learning Optimizer Benchmark Suite Schneider, F., Balles, L., Hennig, P. 7th International Conference on Learning Representations (ICLR), May 2019 (Published) URL BibTeX
Probabilistic Numerics Empirical Inference Conference Paper Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization de Roos, F., Hennig, P. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89:1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (Published) PDF URL BibTeX
Probabilistic Numerics Empirical Inference Conference Paper Fast and Robust Shortest Paths on Manifolds Learned from Data Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89:1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (Published) PDF URL BibTeX
Probabilistic Numerics Conference Paper Kernel Recursive ABC: Point Estimation with Intractable Likelihood Kajihara, T., Kanagawa, M., Yamazaki, K., Fukumizu, K. Proceedings of the 35th International Conference on Machine Learning, 2405-2414, PMLR, July 2018 Paper BibTeX
Empirical Inference Probabilistic Numerics Conference Paper Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S. Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML) at ICML, July 2018 (Published) BibTeX
Probabilistic Numerics Conference Paper Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients Balles, L., Hennig, P. Proceedings of the 35th International Conference on Machine Learning (ICML), 80:404-413, Proceedings of Machine Learning Research, (Editors: Jennifer Dy and Andreas Krause), PMLR, ICML, July 2018 (Published) URL BibTeX
Autonomous Motion Probabilistic Numerics Intelligent Control Systems Conference Paper On the Design of LQR Kernels for Efficient Controller Learning Marco, A., Hennig, P., Schaal, S., Trimpe, S. Proceedings of the 56th IEEE Annual Conference on Decision and Control (CDC), 5193-5200, IEEE, IEEE Conference on Decision and Control, December 2017 (Published) arXiv PDF On the Design of LQR Kernels for Efficient Controller Learning - CDC presentation DOI BibTeX
Empirical Inference Probabilistic Numerics Conference Paper Dynamic Time-of-Flight Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S. Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, 170-179, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (Published) DOI BibTeX
Probabilistic Numerics Conference Paper Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54:528-536, Proceedings of Machine Learning Research, (Editors: Sign, Aarti and Zhu, Jerry), PMLR, April 2017 (Published) pdf URL BibTeX
Probabilistic Numerics Conference Paper Active Uncertainty Calibration in Bayesian ODE Solvers Kersting, H., Hennig, P. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), 309-318, (Editors: Ihler, Alexander T. and Janzing, Dominik), June 2016 (Published) URL BibTeX
Probabilistic Numerics Conference Paper Batch Bayesian Optimization via Local Penalization González, J., Dai, Z., Hennig, P., Lawrence, N. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51:648-657, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C.), May 2016 (Published) URL BibTeX
Probabilistic Numerics Conference Paper Probabilistic Approximate Least-Squares Bartels, S., Hennig, P. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51:676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), May 2016 (Published) URL BibTeX
Autonomous Motion Empirical Inference Probabilistic Numerics Intelligent Control Systems Conference Paper Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S. Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (Published) PDF DOI BibTeX